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Progress Report on TT03 by the Institute for Language and Information Technologies (ILIT), UMBC

Progress Report on TT03 by the Institute for Language and Information Technologies (ILIT), UMBC. Sergei Nirenburg Marge McShane. Past Work: Annotating Modality An analyst will benefit from being able to distinguish what Al-Qaeda can/might/is trying to do from

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Progress Report on TT03 by the Institute for Language and Information Technologies (ILIT), UMBC

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  1. Progress Report on TT03 by the Institute for Language and Information Technologies (ILIT), UMBC Sergei Nirenburg Marge McShane

  2. Past Work: Annotating Modality An analyst will benefit from being able to distinguish what Al-Qaeda • can/might/is trying to do from • can’t, didn’t, should, shouldn’t do from • evaluative judgments of its actions. • Al-Qaeda has not infiltrated the CIA. • Al-Qaeda might infiltrate the CIA. • Al-Qaeda can infiltrate the CIA. • Al-Qaeda cannot infiltrate the CIA. • It is unlikely that Al-Qaeda has infiltrated the CIA. • Al-Qaeda should infiltrate the CIA. • Al-Qaeda should not infiltrate the CIA. • Al-Qaeda is trying to infiltrate the CIA • Al-Qaeda is not trying to infiltrate the CIA. • Al-Qaeda wants to infiltrate the CIA. • Al-Qaeda does notwant to infiltrate the CIA. • It is appalling that Al-Qaeda might infiltrate the CIA.

  3. Application-specific examples of the utility of recognizing modalities • Knowledge extraction/summarization: Extract and return only those propositions that are or are not in the scope of a given type of modality (volitive, potential, permissive, obligative, etc.): e.g., everything some organization should do, cannot do, etc. • Q/A: Answer questions that ask about a specific type of entity or event • Visualization: Color-code or otherwise highlight search results by modality type • Profiling: Create entity or event profiles by modality type • Translation: Detect and render paraphrases of modal expressions • Reasoning: Correctly constrain inferences

  4. Current Work: Overview • Goal: To conduct research to automatically detect travel events in which certain people participate. • Resources: The database of Enron e-mail materials, as preprocessed by the Columbia University team; the Stanford parser; the OntoSem semantic analysis engine. • Output/Evaluation: Concentrate on precision rather than recall.

  5. Work to Date • Improving and expanding our heuristics for reference resolution and the engine that uses them. • E.g., if the referring expression and the candidate antecedent both occupy the subject or object position AND they are in a clausal conjunction structure AND this candidate is the nearest possible candidate that sufficiently fits the given semantic constraints (as determined by semantic analysis and the ontology) THEN we can accept the candidate as the antecedent with a very high level of confidence. Weaker combinations of heuristics reduce the confidence level of the choice of antecedent. • Why work on reference resolution? In order to be able to exploit information about the travel of certain people even if their names are not in the same sentence as the mention of the travel event.

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